Horticultural temporal fruit monitoring via 3D instance segmentation and re-identification using colored point clouds

Abstract

Accurate and consistent fruit monitoring over time is a key step towards automated agricultural production systems. However, this task is inherently difficult due to variations in fruit size, shape, occlusion, orientation, and the dynamic nature of orchards where fruits may appear or disappear between observations. In this article, we propose a novel method for fruit instance segmentation and re-identification on 3D terrestrial point clouds collected over time. Our approach directly operates on dense colored point clouds, capturing fine-grained 3D spatial detail. We segment individual fruits using a learning-based instance segmentation method applied directly to the point cloud. For each segmented fruit, we extract a compact and discriminative descriptor using a 3D sparse convolutional neural network. To track fruits across different times, we introduce an attention-based matching network that associates fruits with their counterparts from previous sessions. Matching is performed using a probabilistic assignment scheme, selecting the most likely associations across time. We evaluate our approach on real-world datasets of strawberries and apples, demonstrating that it outperforms existing methods in both instance segmentation and temporal re-identification, enabling robust and precise fruit monitoring across complex and dynamic orchard environments. • We propose a new performant approach to autonomous fruit tracking in real greenhouses. • It segments fruits using learning-based instance segmentation and RGB 3D point clouds. • Segmented fruits are encoded by a 3D CNN and matched via attentive data association. • Experiments on real strawberry and apple datasets show our method outperforms others. • Our approach enables precise temporal fruit monitoring in real and complex scenarios.

Plant phenotyping relevance

果実を個体単位で3D点群からセグメンテーションし、時系列追跡する画像解析手法の開発・評価が研究の中心であり、植物器官の状態を抽出するため適格。

Code and data availability

The paper explicitly states that the authors' implementation of the fruit matching method (IRIS3D) is publicly available on GitHub, which is the computational analysis code for this paper's fruit segmentation and re-identification phenotyping pipeline.